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<table width="100%" summary="page for hbk"><tr><td>hbk</td><td style="text-align: right;">R Documentation</td></tr></table>

<h2>Hawkins, Bradu, Kass's Artificial Data</h2>

<h3>Description</h3>

<p>Artificial Data Set generated by Hawkins, Bradu, and Kass (1984).  The
data set consists of 75 observations in four dimensions (one response
and three explanatory variables).  It provides a good example of the
masking effect.  The first 14 observations are outliers, created in
two groups: 1&ndash;10 and 11&ndash;14.
Only observations 12, 13 and 14 appear as outliers when using
classical methods, but can be easily unmasked using robust
distances computed by, e.g., MCD - covMcd().
</p>


<h3>Usage</h3>

<pre>data(hbk, package="robustbase")</pre>


<h3>Format</h3>

<p>A data frame with 75 observations on 4 variables, where the last
variable is the dependent one.
</p>

<dl>
<dt>X1</dt><dd><p>x[,1]</p>
</dd>
<dt>X2</dt><dd><p>x[,2]</p>
</dd>
<dt>X3</dt><dd><p>x[,3]</p>
</dd>
<dt>Y</dt><dd><p>y</p>
</dd>
</dl>



<h3>Note</h3>

<p>This data set is also available in package <span class="pkg">wle</span> as
<code>artificial</code>.
</p>


<h3>Source</h3>

<p>Hawkins, D.M., Bradu, D., and Kass, G.V. (1984)
Location of several outliers in multiple regression data using
elemental sets.
<em>Technometrics</em> <b>26</b>, 197&ndash;208.
</p>
<p>P. J. Rousseeuw and A. M. Leroy (1987)
<em>Robust Regression and Outlier Detection</em>;
Wiley, p.94.
</p>


<h3>Examples</h3>

<pre>
data(hbk)
plot(hbk)
summary(lm.hbk &lt;- lm(Y ~ ., data = hbk))

hbk.x &lt;- data.matrix(hbk[, 1:3])
(cHBK &lt;- covMcd(hbk.x))
</pre>


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